Nanomedicine and the tumor immune microenvironment in cancer immunotherapy: a bibliometric and knowledge-mapping analysis

Background Nanomedicine can improve cancer immunotherapy by enabling tumor-selective delivery and active modulation of the tumor immune microenvironment (TIME), yet the global structure and translational trajectory of this interdisciplinary field remain unclear. Methods The Web of Science Core Collection and Scopus were searched from inception to February 1, 2025. After deduplication and screening, 1,110 English-language articles and reviews were included. VOSviewer, CiteSpace, Bibliometrix, and Python were used to analyze publication trends, collaboration networks, journal relationships, co-citation structures, citation bursts, and keyword evolution. Non-negative matrix factorization was applied to citation-burst references to identify latent thematic structures. Results Publication output increased sharply after 2022, and studies published during 2022–2025 accounted for 79.9% of the dataset. China led in publication volume and institutional productivity, whereas the United States showed strong citation influence. Soochow University, the Chinese Academy of Sciences, and Sichuan University were the most productive institutions. Three major themes emerged: photodynamic therapy combined with immune checkpoint blockade; tumor-microenvironment remodeling and melanoma immunotherapy; and nanomaterial-mediated immune activation involving cell death, antigen release, and adaptive antitumor responses. Temporal mapping demonstrated a shift from passive nanoparticle delivery toward active immune-regulatory nanoplatforms. Conclusion By integrating dual-database bibliometrics, citation-burst analysis, temporal knowledge mapping, and topic modeling, this study clarifies the intellectual structure and evolution of nano-immuno-oncology. Future research should prioritize biologically predictive models, reproducible manufacturing, standardized characterization, long-term safety assessment, biomarker-guided patient selection, and clinically scalable designs.